Center for Molecular Modeling - P. Dobbelaere

Cluster-Based Machine Learning Potentials to Describe Disordered MetalOrganic Frameworks up to the Mesoscale

leen — Tue, 05 Aug 2025 11:00:10 +0000

P. Dobbelaere, S. Vandenhaute, V. Van Speybroeck

Chemistry of Materials

37, 15, 5696-5709

2025

Abstract

Metal-organic frameworks (MOFs) are highly interesting and tunable materials. By incorporating spatial defects into their atomic structure, MOFs can be finetuned to exhibit precise chemical functionalities, extending their applicability in various technological fields. Defect engineering requires a fundamental understanding of the nature of spatial disorder and consequent changes in material properties, which is currently lacking. We introduce the cluster-based learning methodology, enabling the development of state-of-the-art machine learning potentials (MLPs) from defective systems at any length scale. Our method identifies atomic interactions in bulk structures and extracts local environments as finite molecular fragments to augment the model's training data where needed. We show that cluster-based learning delivers MLPs capable of accurately describing spatial defects in mesoscopic systems with over 20 thousand atoms. Afterward, we select our best model to investigate some major mechanical properties of spatially disordered UiO-66-derived structures, elucidating the influence of defect concentration and composition on material behavior. Our analysis includes large supercell structures, demonstrating that (near-) ab initio accuracy is within reach at the mesoscale.

Gold Open Access

DOI

http://dx.doi.org/10.1021/acs.chemmater.5c00821

Private attachment

dobbelaere-et-al-2025-cluster-based-machine-learning-potentials-to-describe-disordered-metal-organic-frameworks-up-to.pdf

MIL-91(Al) to Boost Solid–Solid Conversion Reactions in Li-Se Batteries

leen — Thu, 22 May 2025 08:28:22 +0000

T. Mutlu-Cetinkaya, P. Dobbelaere, W. Temmerman, W. Lu, V. Pimenta, V. Van Speybroeck, R. Demir-Cakan

Energy & Environmental Materials

8, 5

2025

Abstract

Lithium-Selenium (Li-Se) batteries have emerged as one of the most promising candidates for next-generation energy storage systems owing to superior electronic conductivity, impressive volumetric capacity, and enhanced compatibility with carbonate electrolyte of selenium, comparable to sulfur. Despite these advantages, the development of Li-Se batteries is impeded by several intrinsic challenges, including volume expansion during the discharge process and the consequent sluggish reaction kinetics that undermine their electrochemical performance. In this study, MIL-91(Al) is used as an electrode additive to accelerate the one-step mutual solid–solid conversion reaction between Se and Li₂Se in the carbonate-based electrolyte. By doing so, uncontrollable deposition of Li₂Se is effectively mitigated, enhancing the electrochemical performance of the system. Thus, the use of MIL-91(Al) results in reduced internal resistance and faster Li-ion transfer rate, as analyzed by SPEIS and GITT. Ab initio calculations and molecular dynamics simulations further reveal that Li₂Se anchors to closely situated dangling oxygens of the phosphonate group of the organic linker of MIL-91(Al), inducing relaxation of the Li-Se-Li angle and stabilizing the overall structure. Accordingly, the MIL-91(Al)-containing Li-Se cells demonstrate a high specific capacity of approximately 530 mAh g⁻¹ at 1C (675 mA g⁻¹) after 100 cycles and retaining a specific capacity of 320 mAh/g even under high current rate (20C) after 200 cycles. This research underlines the importance of the use of electrocatalyst/electroadsorbent materials to enhance the redox kinetics of the conversion reactions between Se and Li₂Se, thus paving the way for the development of high-performance Li-Se batteries.

Gold Open Access

DOI

https://doi.org/10.1002/eem2.70038

Private attachment

MIL‐91 Al to Boost Solid Solid Conversion Reactions in Li‐Se.pdf

Cluster-based learning to describe disordered metal-organic frameworks at the mesoscale

pidobbel — Tue, 14 Oct 2025 09:20:15 +0000

P. Dobbelaere, S. Vandenhaute, V. Van Speybroeck

384

ISBN/ISSN:

Poster

Conference / event / venue

EUROMOF2025

Heraklion Greece

Friday, 19 September, 2025 to Wednesday, 24 September, 2025

Abstract (private)

Abstract_EUROMOF2025_PieterDobbelaere.docx_.pdf

Pushing the limits of computational chemistry: the phase diagram of water

massimo — Wed, 01 Oct 2025 07:05:26 +0000

M. Bocus, T. Braeckevelt, P. Dobbelaere, A. Maet, W. Temmerman, S. Vandenhaute, S. Vanlommel, J. Vekeman, V. Van Speybroeck

ISBN/ISSN:

Invited talk

Conference / event / venue

EuroHPC User Days 2025

Copenhagen, Denmark

Tuesday, 30 September, 2025 to Wednesday, 1 October, 2025

Cluster-based learning for disordered metal-organicframeworks at the mesoscale

mieke — Thu, 18 Apr 2024 14:42:22 +0000

P. Dobbelaere, S. Vandenhaute, V. Van Speybroeck

ISBN/ISSN:

Poster

Conference / event / venue

MOFSIM2024

Montpellier, France

Wednesday, 10 April, 2024 to Friday, 12 April, 2024

Conference reference

MOFSIM2024

It's all about time: Understanding the impact of coarse graining on accelerated diffusion in nanoporous materials

leen — Thu, 24 Mar 2022 09:57:56 +0000

P. Dobbelaere

Master of Science in Engineering Physics

2021

Supervisors

Prof. Dr. ir. V. Van Speybroeck

Dynamic nano-sponges: nuclear quantum effects meet neural network potentials

leen — Thu, 17 Mar 2022 10:43:49 +0000

Pieter Dobbelaere

samuel — Thu, 10 Sep 2020 08:50:43 +0000